An advanced approach for adaptive nonlinear digital data processing is described in this article. Three primal
computational structures referred to as Q-Measures, Q-Metrics, and Q-Aggregates are introduced and utilized in unison
as highly adaptive data analysis handlers. The proposed approach relies on universal functionals using few parameters to
characterize dynamic system behaviors in broad ranges of unconventional measure, metric, and aggregation spaces. We
present this unique approach in application to real-valued signal processing tasks, with suitable optimization algorithms,
so that the parameters of the proposed models can be tuned automatically. The new approach is tested on real data sets to
enable applications in mobile communication systems and the experiments show promising results.